Genome-wide meta-analysis identi
fies eight new
susceptibility loci for cutaneous squamous cell
carcinoma
Kavita Y. Sarin
1
*, Yuan Lin
2,13
, Roxana Daneshjou
1,13
, Andrey Ziyatdinov
3
, Gudmar Thorleifsson
4
,
Adam Rubin
1
, Luba M. Pardo
5
, Wenting Wu
2
, Paul A. Khavari
1
, Andre Uitterlinden
6
, Tamar Nijsten
5
,
Amanda E. Toland
7
, Jon H. Olafsson
8,9
, Bardur Sigurgeirsson
8,9
, Kristin Thorisdottir
8,9
, Eric Jorgensen
10
,
Alice S. Whittemore
11
, Peter Kraft
3
, Simon N. Stacey
4
, Kari Stefansson
4,9
, Maryam M. Asgari
12
&
Jiali Han
2
*
Cutaneous squamous cell carcinoma (SCC) is one of the most common cancers in the United
States. Previous genome-wide association studies (GWAS) have identi
fied 14 single
nucleotide polymorphisms (SNPs) associated with cutaneous SCC. Here, we report the
lar-gest cutaneous SCC meta-analysis to date, representing six international cohorts and totaling
19,149 SCC cases and 680,049 controls. We discover eight novel loci associated with SCC,
con
firm all previously associated loci, and perform fine mapping of causal variants. The novel
SNPs occur within skin-speci
fic regulatory elements and implicate loci involved in cancer
development, immune regulation, and keratinocyte differentiation in SCC susceptibility.
https://doi.org/10.1038/s41467-020-14594-5
OPEN
1Department of Dermatology, Stanford University School of Medicine, 450 Broadway St, C-229, Redwood City, CA 94305, USA.2Department of Epidemiology, Richard M. Fairbanks School of Public Health, Melvin & Bren Simon Cancer Center, Indiana University, 1050 Wishard Blvd, Indianapolis, IN 46202, USA.3Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.4deCODE genetics/Amgen Inc., Sturlugata 8, 101 Reykjavik, Iceland.5Department of Dermatology, Erasmus University Medical Center, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.6Department of Internal Medicine, Erasmus University Medical Center, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.7Departments of Cancer Biology and Genetics and Department of Internal Medicine, Division of Human Genetics, Comprehensive Cancer Center, Ohio State University, 460W. 12th Ave, Columbus, OH 43420, USA.8Landspitali-University Hospital, Skaftahild 24, 105 Reykjavik, Iceland.9Faculty of Medicine, University of Iceland, Vatnsmyrarvegur 16, 101 Reykjavik, Iceland.10Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.11Departments of Epidemiology and Population Health and of Biomedical Data Sciences, Stanford University School of Medicine Redwood Bldg, T204, Stanford, 94305 CA, USA.12Department of Dermatology, Massachusetts General Hospital, 50 Staniford Street, Suite 270, 02114 Boston, MA, USA.13These authors contributed equally: Yuan Lin, Roxana Daneshjou. *email:ksarin@stanford.edu;jialhan@iu.edu
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C
utaneous squamous cell carcinoma (SCC) is one of the
most common cancers with an estimated 700,000 cases
diagnosed in the USA annually. Metastatic SCC is
responsible for 3900–8800 deaths annually in the USA
1,2. Risk
factors for SCC include age, gender, fair skin pigmentation
phe-notypes, ultraviolet radiation exposure, and
immunosuppres-sion
3. While the risk factors for SCC development have largely
been attributed to environmental exposures and skin
pigmenta-tion, there has been a growing appreciation of the contribution of
germline genetics in SCC development.
Recently, three genome-wide association studies (GWAS) have
identified 14 single-nucleotide polymorphisms (SNPs) associated
with cutaneous SCC
4–6. These studies include a GWAS in 7404
SCC cases and 292,106 controls in the 23andMe, the Nurses’
Health Study (NHS) and the Health Professionals Follow-Up
Study (HPFS) cohort
4, a GWAS in 7701 SCC cases and 60,186
controls from the Kaiser Permanente Northern California
healthcare system
6, and a GWAS in 745 SCC cases and 12,805
controls from Rotterdam Study, NHS, and HPFS
5. These 14 SNPs
involve loci which affect skin pigmentation, but also occur in loci
associated with cell-mediated immunity, anti-apoptotic pathways
and cellular proliferation.
Unfortunately, further identification of SCC risk loci has been
hampered by a lack of well-phenotyped cohorts and a cancer
registry for cutaneous SCC. To aid in this, we developed a
SCC-GWAS consortium comprised six international cohorts with data
on cutaneous SCC. Here, we present the results of the largest
cutaneous SCC meta-analysis to date, totaling 19,149 SCC cases
and 680,049 controls. We discover eight novel loci associated with
cutaneous SCC, confirm all previously associated loci, and
per-form
fine mapping of causal variants. The novel SNPs occur
within skin-specific regulatory elements and implicate loci
involved in cancer development, immune regulation, and
kera-tinocyte differentiation in SCC susceptibility.
Results and discussion
Cohort description. The GWAS meta-analysis consisted of
19,149 SCC cases and 680,049 controls, including 2081 SCC cases
and 296,015 controls from deCODE genetics in Iceland, 398 cases
and 10,629 controls from Rotterdam, Netherlands, 6579 cases and
280,558 controls from 23andMe, 2287 cases and 30,966 controls
from NHS/HPFS, 103 cases and 1715 controls from Ohio State
University Hospital, and 7701 cases and 60,166 controls from
Kaiser Permanente. Demographics and further details on these
studies are found in the
“Methods” and Supplementary Tables 1
and 2.
Genome-wide signi
ficant novel susceptibility loci. This
meta-analysis reinforced all 14 previously described loci associated with
cutaneous SCC (Fig.
1
; Supplementary Table 3). Recently a
C-terminal exon mutation in the BRCA2 gene (K3326*, rs11571833)
was reported to confer risk of SCC
7. We examined the
meta-analysis data and found that rs11571833 is associated with SCC
with an effect size of 0.36 (log odds ratio) for the alternate
(minor) allele and p-value 1.0 × 10
−6, confirming the reported
observation and highlighting the contribution of DNA repair
genes to SCC risk.
In addition to confirming all previous susceptibility loci (Fig.
1
;
Supplementary Table 3), this meta-analysis identified eight novel
susceptibility loci for cutaneous SCC: rs10399947 (1q21.3),
rs10200279 (2q33.1), rs10944479 (6q15), rs7834300 (8q23.3),
rs1325118 (9p23), rs7939541 (11p15.4), rs657187 and rs11170164
(12q13.13), rs721199 (12q23.1) (Table
1
; Supplementary Tables 4,
5). Forest plots of the individual GWAS study results are detailed
in Supplementary Figs. 3A–3V. Regional association plots are
found in Supplementary Figs. 4A–4V. These loci included genes
involved in cancer progression (SETDB1: rs10399947, CASP8/
ALS2CR12: rs10200279, WEE1: rs7939541), immune regulation
(BACH2: rs10944479), keratinocyte differentiation (TRPS1:
rs7834300, KRT5: rs11170164 and rs657187), and pigmentation
(TYRP1: rs1325118). These loci are discussed in detail below.
Fine-mapping resolution at the associated loci. We sought to
refine the localization of potential functional variants in the 22
genome-wide significant loci using a Bayesian approach
(Meth-ods). Conditional analyses in 18 of the 22 identified loci revealed
21 distinct association signals or index SNPs with p < 5 × 10
−8(Supplementary Table 6, Supplementary Table 7). We further
estimated 99% the credible sets for every index SNP in 18 loci.
We excluded two loci from conditional analysis: the locus 6p21.32
was excluded as this is an HLA locus. The MC1R locus at
16q24.3 showed evidence of a large number of SNPs (24) driving
the association, suggesting, in part the presence of allelic
het-erogeneity
8. This is consistent with previous studies including a
recent GWAS in the UK Biobank, which found 31 SNPs
inde-pendently associated with red hair color near MC1R, of which
only 10 were coding variants
9,10. Due to allelic complexity and
potential artifacts with an external LD reference panel, this locus
was also excluded from conditional analysis. We found that the
number of SNPs in the sets across 18 loci ranges from 1 to 1990
with a mean value of 136. The lead SNP at seven signals
accounted for >0.80 of posterior probability of association (PPA,
Methods) and, at six of these signals including rs7939541 in the
novel 11p15.4 locus, PPA exceeded 0.99.
Fine mapping revealed three loci with distinct secondary
signals: rs6935510, rs10962599, and rs4778138. rs6935510 at
locus 6p25.3 (r
2= 0.12 from the lead SNP rs12203592 in CEU
population) is 2 kb upstream of IRF4 in a predicted bivalent
promoter region and alters a number of regulatory motifs. IRF4 is
a transcription factor downstream of MITF and is associated
with photosensitivity, freckles, blue eyes, and brown hair color
11.
rs10962599, an intronic variant in the skin pigmentation gene
BNC2 at 9p22.2, independent from lead SNP rs10810657 (r
2=
0.0012 in CEU population) and in a H3K4me1 enhancer region
in melanocytes. rs4778138 at 15q13.1 is independent from the
lead SNP rs1800407 (r
2= 0.0032 in CEU population). rs4778138
is an intronic variant in a novel locus, OCA2, and has been
implicated in melanoma risk, hair and eye color
12–14.
SNPs associated with pigmentation and photodistributed sites.
Fair skin and sun exposure are well-described risk factors for
SCC. We analyzed the 22 SCC risk loci for an association with
pigmentation phenotypes in the deCODE cohort, including eye
color, hair color, freckling, and photosensitivity (Supplementary
Table 8). Pigmentation information was self-reported as
pre-viously described
15,16. Nine out of 22 index SNPs were associated
with pigmentation phenotypes, including two novel SNPs;
rs7834300, an intronic SNP in TRPS1 associated with sun
sen-sitivity, and rs1325118, located 66 kb upstream of TYRP1 and is
associated with eye color
17.
Although sun exposure information was not available for
the majority of cohorts, we sought to determine potential
gene–environment interactions by performing a site-stratified
analysis of SCC risk loci to determine SNPs associated with SCC
in photodistributed sites. Cohorts with SCC site information
(deCODE, NHS/HPFS, Rotterdam, and Ohio) were divided into
high photoexposure (head and neck, upper extremities) and low
photoexposure sites (trunk and legs) based on site location of the
first SCC. We observed one SNP, rs721199, in which the T allele
was specifically protective against SCC in low-photodistributed
sites (Supplementary Table 9). rs721199 is an eQTL in skin tissue
for HAL (sun-exposed lower leg skin, p
= 4.1 × 10
−79and
sun-exposed suprapubic skin 1.2 × 10
−67) which has been shown to
play a role in UV radiation mediated immunosuppression. This
highlights a potential gene–environment interaction which
contributes to SCC development.
Heritability of SCC. We estimated the overall contribution of
common variants to SCC risk using LD Score Regression
18.
Approximately 25% (95% confidence interval 0.17–0.32) of the
familial relative risk for SCC can be explained by common
var-iants across the genome. In contrast, the 22 genome-wide
sig-nificant loci explain 8.5% of the familial relative risk. This
suggests that there are additional SCC risk loci that could be
identified in a larger GWAS. We also used LD Score Regression to
explore whether particular regions of the genome
dis-proportionately contributed to the overall common-variant
her-itability. We partitioned common-variant heritability across 53
publicly
available,
non-cell-type-specific annotations and
observed significant enrichment in heritability (FDR < 0.1) for
coding regions (6.7 × enrichment, p
= 8.5 × 10
−4), super
enhan-cers (2.1×, p
= 1.2 × 10
−3), and H3K4me3 histone promoter
marks (1.7 × p
= 5.5 × 10
−3). Heritability in repressed regions was
significantly depleted (0.5×, p = 8.5 × 10
−3) (Supplementary
Table 10)
19. We also conducted enrichment analyses using 220
cell-type-specific histone marks; none of these marks were
sig-nificantly enriched (Supplementary Table 11)
19. These
findings
highlight the increased contribution to SCC risk from variants,
which affect protein coding and gene regulation.
Description of novel loci. At 1q21.3, rs10399947 has a PPA of
0.02, and is an eQTL for multiple genes in skin tissue, including
SETDB1, ECM1, and CERS2 (Supplementary Table 12). SETDB1
encodes a histone methyltransferase and is associated with the
propagation of several malignancies, including melanoma
20,21.
ECM1 codes for the extracellular matrix protein 1, and has been
found to be overexpressed in epithelial malignancies as well as
melanoma cell lines
22,23. CERS2 encodes ceramide synthase 2 and
is thought to inhibit metastases and invasion across multiple
cancer types, including breast cancer
24.
–Log10( p -value)
*
*
*
*
*
** *
SETDB1 3.0 2.5 2.0 1.5 1.0 0.5 0.0 Chr 1 Chr 13 Chr 2 Chr 14 Chr 3 Chr 15 Chr 4 Chr 16 Chr 5 Chr 17 Chr 6 Chr 18 Chr 7 Chr 19 Chr 8 Chr 20 Chr 9 Chr 21 Chr 10 Chr 22 Chr 11 Chr 12 Chr X CASP8/ ALS2CR12 BACH2 TRPS1 TYRP1 WEE1 KRT5/KRT6B HALFig. 1 Manhattan plot of the combined meta-analysis of GWAS of SCC. The PfixedStage one value for all SNPs present in at least two studies have been plotted using a−log10(p-value). The total Stage one meta-analysis included eight SCC GWAS, totaling 19,149 cases and 680,049 controls. p < 5 × 10–8 (genome-wide significance) threshold is indicated by a dashed line. In total, 22 loci reached genome-wide significance, including 8 novel loci 1q21.3, 2q33.1, 6q15, 8q23.3, 9p23, 11p15.4, 12q13.3, and 12q23.1 are highlighted by *.
Table 1 Novel associations in SCC-GWAS meta-analysis.
SNP Chr Position Locus Gene Major allele Minor allele MAF Odds ratio (95% CI) Direction p-value rs10399947 1 150861960 1q21.3 ARNT--[]--SETDB1 G A 0.368 0.94 (0.92–0.96) −, −, −, −, −, + 6.65E-09 rs10200279 2 202170655 2q33.1 [ALS2CR12] C T 0.287 1.07 (1.05–1.10) +,+,+,+,+, + 2.67E-09 rs10944479 6 90880393 6q15 [BACH2] G A 0.189 0.91 (0.89–0.94) −, −, −, −, −, N 3.75E-09 rs7834300 8 116611632 8q23.3 [TRPS1] C G 0.438 1.07 (1.05–1.09) +,+,+,+, −,+ 2.01E-09 rs1325118 9 12619616 9p23 []--TYRP1 T C 0.304 0.94 (0.91–0.96) −, −, −, −, +, − 4.38E-08 rs7939541 11 9590389 11p15.4 ZNF143--[]--WEE1 T C 0.410 1.08 (1.06–1.10) +,+,+,+,+, + 9.23E-12 rs657187 12 52898985 12q13.13 KRT6A--[]--KRT5 A G 0.420 0.93 (0.92–0.96) −, −, −, −, −, − 1.80E-09 rs721199 12 96374057 12q23.1 [HAL] C T 0.463 0.94 (0.92–0.96) −, −, −, −, −, − 3.55E-08
MAF: minor allele frequency, CI: confidence interval, build GRCh37. [] represents location of SNP either in relationship to known genes with [gene] indicating SNP is within the gene and gene—[]— indicating intergenic SNPs. Minor allele is effect allele. Minor allele frequency (MAF) is based on the pooled meta-analysis. Direction is listed in order for 23me, deCODE, NHS/HPFS, Kaiser, Ohio, and Rotterdam. N means not included in analysis.
At 2q33.1, rs10200279 has a PPA of 0.12 and is an intronic SNP
of ALS2CR12, an eQTL in skin tissue for CASP8, ALS2CR12,
CASP10, and PPIL3 and alters six regulatory motifs
(Supplemen-tary Table 12)
25,26. The CASP8/ALS2CR12 locus has been
implicated in multiple cancer types, including basal cell carcinoma
and breast cancer
27–29. CASP10 is a homologue for CASP8 and has
been found to inhibit tumorigenesis; loss-of-function mutations
have been reported in multiple cancer types. PPIL3 is proximal to
CASP8 and has been independently associated with estrogen
receptor-negative breast cancer
30. rs10200279 is LD with rs700635
(PPA 0.08, r
2= 0.97 in European 1000G Phase 1 population),
which has been associated with basal cell carcinoma risk and
shown to functionally affect splicing of the cellular apoptosis
regulator, CASP8
27,29,31. Ten SNPs had a PPA threshold of 0.05
and could also represent potential causal variants. These are listed
in Supplementary Table 13. Interestingly, all of them are eQTLs in
the skin tissue for CASP8 and ALS2CR12.
At 6q15, rs10944479 has a PPA of 0.29 and is an intronic SNP of
BACH2, which encodes a transcription factor involved in tumor
immunosuppression and response to anti-PD-1 treatment
32,33.
This SNP alters two predicted regulatory motifs (HNF6 and
Hoxa10)
17. Expression of BACH2 was suppressed by 57% in SCC
as compared with paired matched normal skin (p
= 6.8 × 10
−9)
highlighting a potential mechanism by which SCC could evade
immune surveillance (Fig.
2
).
At 8q23.3, rs7834300 has a PPA of 0.05 and is an intronic
variant in TRPS1, a sequence-specific transcriptional repressor
important for bone, hair follicle, and kidney differentiation.
Recently, TRPS1 has been associated with tanning response
34.
rs7834300 alters two regulatory motifs (GR, Zec)
17,25. In the
deCODE cohort, this variant was associated with sun sensitivity
(Supplementary Table 8).
At 9p23, rs1325118 has a PPA of 0.5 in our analysis and is 66 kb
upstream of TYRP1, a pigmentation gene and alters three predicted
regulatory motifs. In the deCODE cohort, rs1325118 was also
associated with eye pigmentation (Supplementary Table 8)
35. In
SCC samples, expression of TYRP1 was suppressed 58% as
compared with matched normal skin biopsies (p
= 3 × 10
−5),
suggesting that keratinocytes in SCC may have defects in
differentiation and contain reduced pigmentation (Fig.
2
).
At 11p15.4, rs7939541 accounts for over 99% of the PPA at this
locus and is 5.8 kb upstream of WEE1. It is in an enhancer feature
and is an eQTL in skin tissue for WEE1, snoU13 (Supplementary
Table 12), alters two predicted regulatory motifs and is in a
DNAse hypersensitivity site for multiple tissues, including the
skin. This SNP falls in a region marked by H3K27ac and
H3K4me1 enhancer-associated histone marks, with lack of the
repressive H3K27me3 mark in primary keratinocytes (Fig.
3
). In
addition, WEE1 transcript levels were suppressed in SCC as to the
normal skin (Fig.
3
, p
= 0.0002) WEE1 encodes a kinase that is a
G2-M checkpoint inhibitor and is highly expressed in multiple
cancer types, including melanoma and non-cutaneous squamous
cell carcinoma
36,37. WEE1 inhibition can increase the sensitivity
of several different cancer types to radiation or chemotherapy
36.
At 12q13.13, rs657187 has a PPA of 0.22, is 9.4 kb 3ʹ of KRT5,
and alters two predicted regulatory motifs
17,25. It is also an
enhancer feature in the skin and an eQTL of KRT6C in the skin
(Supplementary Table 12), a keratinocyte development gene
17,38.
Expression of KRT6C was 8.5 times higher in SCC as compared
with the normal skin (Fig.
2
, p
= 5.51 × 10
−13). rs657187 is in low
LD (r
2= 0.052 in CEU) with rs11170164 (PPA = 0.01), a nearby
SNP which encodes a G138E substitution in KRT5 and has been
previously associated with BCC and SCC
39. Conditional analysis
of rs657187 and rs11170164 indicated that these variants each
have independent effects (p
adj= 2.28 × 10
−6and 5.67 × 10
−5,
respectively, Supplementary Table 14).
At 12q23.1, rs721199 has a PPA of 0.36, is an intronic SNP of
HAL, alters three predicted regulatory motifs, and is an eQTL in
the skin tissue for HAL and RP11-256L6.3 (Supplementary
Table 12)
17,25. HAL is highly expressed in the skin and plays a
role in UV-mediated immunosuppression
40. In the stratification
analysis by photodistributed site (high or low), the protective
–6 –4 LOG2 [SCC FPKM/NL skin FPKM] –2 0
ALS2CR12 ARNT BACH2 CASP8 HAL KRT5 KRT6C SETDB1 TRPS1 TYRP1 WEE1 ZNF143 2
4 6 8 10
Fig. 2 Gene expression analysis for novel SCC susceptibility loci. RNA-seq data were obtained from Gene Expression Omnibus (GSE84194) were analyzed by DESeq. Transcript levels (FPKM) in SCC samples were compared with levels in paired matched normal skin. Boxplot demonstrates log2[SCC/ Normal skin] expression levels for 13 genes surrounding the novel SNPs. Legend for box and whisker plots. The black center line denotes the median value (50th percentile), while the gray box contains the 25th to 75th percentiles of data set. The black whiskers mark the 5th and 95th percentiles, and values beyond these upper and lower bounds are considered outliers, marked with white circles. The red threshold line indicates the point where these is no change in gene expression between SCC tumor and normal skin. ARNT, BACH2, TYRP1, and WEE1 were significantly downregulated in SCC as compared with normal skin and CASP8 and KRT6C were upregulated in SCC relative to normal skin by DESeq.
association with rs721199 T allele occurred only in the
low-photodistributed site, and the heterogeneity in the effect sizes
among the subgroups was significant (p = 0.03, Supplementary
Table 9). The T allele is associated with higher expression levels
of HAL
26.
Conclusion. In conclusion, this GWAS meta-analysis of 19,149
cases and 680,049 controls from the USA and Europe represents a
threefold increase in sample size compared with the previous
SCC-GWAS studies, and reinforced all 14 previously reported
loci. In addition, this meta-analysis identified eight novel
sus-ceptibility loci. In total, the 22 loci explain 8.5% of heritable risk
for SCC. Subanalyses of these 22 loci identify 9 loci associated
with pigmentation phenotypic traits and 1 locus (HAL) associated
with photodistribution-specific risk. In addition, fine mapping
identifies potentially causal SNPs which fall within putative
reg-ulatory elements in keratinocytes and melanocytes and regulate
the expression of genes involved in cancer progression,
differ-entiation, and immune regulation, highlighting the role of these
pathways in modulating SCC susceptibility.
Methods
Study design. The GWAS meta-analysis is comprised six international cohorts (Supplementary Table 1). The GWAS data set from the personal genetic company 23andMe Inc. encompassed 6579 SCC cases and 280,558 controls of European
ancestry who consented to participate in research. The GWAS data set from the Nurse’s Health Study (NHS)/ Health Professionals Follow-Up Study (HPFS) con-sisted of 2287 SCC cases and 30,966 controls of European ancestry. The 23andMe data and some of the NHS/HPFS data were used in a previously reported GWAS4. Kaiser Permanente Northern California contributed a GWAS set encompassing 7701 cases with incident SCC and 60,166 controls of European ancestry. Some of these data were used in another previously reported GWAS5,6. GWAS data from the deCODE study encompassed 2081 SCC cases and 296,015 controls of European ancestry. The Rotterdam study contributed a GWAS data set consisting of 398 cases with SCC and 10,629 controls of European ancestry. The Ohio study included GWAS data on 103 SCC cases and 1715 controls of European ancestry. Supple-mentary Table 2 shows the gender and age of cases and controls from each cohort. Case validation. Cases were medically adjudicated for the NHS/HPFS, Kaiser, Rotterdam, and Ohio cohorts by histopathologic records. The deCODE cases were ascertained from the Icelandic Cancer Registry, and were all histopathologically confirmed. Cases were self-reported in the 23andMe cohort. In the self-reported cases, survey response accuracy was validated by comparing a subgroup of survey responses with medical record data, which revealed a sensitivity and specificity of 92 and 98%, respectively4.
Genotyping. All samples were collected with informed consent and ethical over-sight. Samples were genotyped on a variety of commercial arrays, as previously detailed4–7,15.
Quality control and imputation. All cohorts underwent strict quality control (QC) procedures and were imputed using the following reference panels: Kaiser cases were imputed using the 1000 Genomes Phase 1 integrated release, March 2012, with Aug 2012 chromosome X update, with singletons removed. 23andMe cases were imputed
Lead SNPs Lead SNPs
+ SNPs with PPA > 0.05
22 50 14
Lead SNPs + SNPs with PPA > 0.05 with KC epigenomic feature
Lead + PPA > 0.05 SNP KC epigenomic feature annotation:
H3K27ac EP Contact
a
b
*
rs7939541 rs6908626 rs7939541 rs3819817 rs3213737 rs657187 rs34936112 rs34619169 rs56360320 rs34302850 rs10747046 rs117132860 rs12212535 rs1007517 rs9865771 WEE1 0 70 H3K4me1 0 450 H3K27ac 0 25 H3K27me3 0 150 CTCF Genes 20 kb Chr11Fig. 3 Annotation of novel SNPs with epidermal enhancer information. a Top: Circles represent the number of SNPs considered at each stage of the workflow to identify epigenetic context of all novel SNPs. We started with 22 lead SNPs identified by meta-GWAS, then found putative causal SNPs defined as any SNPs with a PPA of >0.05 from ourfine-mapping analysis. We next refined that expanded list to SNPs for which the genomic location overlapped a previously identified epigenomic feature (either the H3K27ac enhancer mark or ends of an enhancer–promoter contact). Bottom: Heatmap displaying the overlap of SNPs with enhancer–promoter contacts or H3K27ac marked regions. The blue designation indicates that the SNP overlaps at least one H3K27ac region or contact.b Genome browser tracks for the genomic locus for SCC-index SNP rs793954, PPA > 0.99, demonstrating enhancer features in primary human keratinocytes (KC). ChIP-seq signal tracks are displayed for H3K4me1 and H3K27ac (which typically mark active enhancers and promoters) as well H3K27me3 (which marks inactive loci). Yellow denotes SNP location; note this SNP falls in a region marked by H3K27ac and H3K4me1 enhancer-associated histone marks, with lack of the repressive H3K27me3 mark. CTCF sites indicate that the SNP is not involved in CTCF loops and associated TADs.
using the March 2012 Version 3 release of 1000 Genomes Phase 1 reference hap-lotypes. NHS/HPFS cases were imputed using the 1000 Genomes Project ALL Phase 1 Integrated Release Version 3 (March 2012) Haplotypes with singletons removed. Ohio cases were imputed using 1000 Genomes Phase 3. The Rotterdam cases were imputed using the latest version of Genome of the Netherlands (GoNL) data as the reference. The deCODE data were processed using long-range phasing and impu-tation based on data from the Icelandic population15. Only variants which were found in either the 1000 Genomes Phase 1 Version 3 data set or the Haplotype Reference Consortium data set (version 1.1) were included in the deCODE data. Variants with large differences in frequency between Icelandic and European populations were excluded from the deCODE data. Further information on asso-ciation analysis of individual studies has been reported previously4,6.
Individual genome-wide association analysis. The methods used for association testing in each cohort have been described in detail4–8. Briefly, association analysis was performed using logistic regression, assuming an additive model for allelic effects. Sex and population stratification () were adjusted for by principal component (PC) analysis in each cohort, except deCODE. The deCODE cohort was adjusted differently because it utilizes familial imputation for individuals who have not been directly genotyped7. Five PCs were included to adjust for population stratification in the 23andMe and the NHS/HPFS cohorts. Ten PCs were adjusted for in the Kaiser and the Ohio cohorts. Rotterdam cohort adjusted for the four largest PCs. The linkage disequilibrium (LD) score regression was applied in the deCODE cohort to account for inflation in test statistics due to cryptic relatedness and stratification in the Icelandic population18. Theχ2statistics from GWAS scan were regressed against
LD score and then the intercept was used as a correction factor9.
Meta-analysis. SNPs with imputation quality R2< 0.3 in any data set were excluded
from that individual study prior to meta-analysis. For each study, SNPs with low expected minor allele counts in cases (overall minor allele frequency times number of cases < 10) were also removed before meta-analysis. Fixed-effects meta-analysis was conducted using the METAL software. Heterogeneity of per-SNP effect size in each cohort contributing to overall meta-analyses was assessed using heterogeneity I2
Cochran’s Q statistic (Supplementary Tables 3, 4). The meta-analysis genome-wide inflation value (λ) was 1.06. QQ plots of the GWAS meta-analysis and individual study p-values are provided (Supplementary Tables 3, 4). SNPs were considered significant if they had a p-value less than 5 × 10−8. Individual study p-values are listed in Supplementary Table 5. Effects are given as log odds ratio (β). Proportion of familial relative risk. We estimated the proportion of familial relative risk due to identified, genome-wide significant variants using
P
i^β2iqið1 qiÞ
h i
lnð Þλ ; ð1Þ
where ^βiand qiare the estimated log odds ratio and minor allele frequency for variant i andλ is the familial relative risk for SCC (λ = 2.7)41,42. To estimate the proportion of familial relative risks explained by tagged common variants across the whole genome, we used
^h2obs= P 1 Pð ð ÞÞ
h i
lnðλÞ ; ð2Þ
where ^h2
obsis the estimate of“observed scale” heritability obtained from LD Score Regression applied to the SCC meta-analysis summary statistics (^h2
obs=9.3 × 10−3, SE= 1.5 × 10−3, p= 5.6 × 10−10), and P is the fraction of cases in the overall
sample (2.8%).
Functional annotation of GWAS meta-analyses. We performed linkage dis-equilibrium (LD) score regression analyses using the summary statistics from the meta-analyses of the six GWASes19. We restricted analysis to all SNPs present on the HapMap version 3 data set that had a MAF > 1% and an imputation quality score R2> 0.3 across all studies. LD scores were calculated using the 1000 Genomes
Project Phase 3 EUR reference panel. For stratified analyses taking genomic annotations into account, we created a“baseline model” model with 53 non-cell-type-specific overlapping annotations19. We also performed analyses using 220 cell-type-specific annotations for four histone markers (H3K4me1, H3K4me3, H3K9ac, and H3K27ac) across 27–81 cell types, depending on the histone mar-ker19. For the cell-type-specific analyses, we augmented the baseline model by adding these annotations individually, creating 220 separate models, each with 54 annotations (53+ 1).
Annotation of SNPs with epidermal enhancer site information. The 22 genome-wide significant SNPs as well as SNPs with a posterior probability of association (PPA, Methods) > 0.05 in ourfine-mapping analysis were annotated for enhancer features using our keratinocyte genome-wide promoter capture Hi-C (CHi-C) and H3K27ac ChIP-seq (Fig.3a)43. Enhancer–promoter (EP) contacts and H3K27ac ChIP-seq peaks were derived from Rubin et al.43. SNP locations werefiltered for
direct overlap with H3K27ac peaks or the ends of enhancer–promoter contacts. Contacts were annotated at 10 kb resolution, so SNPs overlapping either 10-kb window marking the ends of a contact were considered overlapped. The WashU Epigenome Browser was used to visualize a SNP and the tracks from the ENCODE Project for NHEK as well as contacts (FDR < 0.01, proximal to the SNP) from progenitor keratinocytes are displayed.
Functional annotation of significant loci. To further annotate regulatory func-tion, PubMed and the NHGRI-EBI GWAS catalogue (version updated 4/10/2018) were queried for prior publications regarding SNP function and disease associa-tion44. We identified the closest related gene and evidence of regulatory function using HaploReg v4.1 (http://archive.broadinstitute.org/mammals/haploreg/ haploreg.php)17. Gene annotations were based on the UCSC Genome Browser and GENCODE version 13.BEDTools was used to calculate the proximity of each variant to a gene by either annotation, as well as the orientation (3' or 5') relative to the nearest end of the gene, based on the strand of the gene. For each index SNP or linked SNP r2≥ 0.8 or SNP with a PPA >0.05, we extracted data on expression
quantitative trait loci (eQTL) for sun-exposed (lower leg) and not sun-exposed (suprapubic area) skin tissue using GTEx portal dbGaP release V826.
Gene expression analysis. Raw RNA-seq data for nine paired matched SCC and normal skin samples biopsied from eight patients. One patient had two SCCs from different sites. (GSE84194 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE84194]) were obtained from the GEO (http://www-ncbi-nlm-nih-gov. laneproxy.stanford.edu/geo/)45. Actinic keratosis samples from this data set were excluded from analysis. Reads were aligned to the human genome (hg19) using Tophat (v2.1.1). Featurecounts (v1.5.2) was used to generate count data and Cufflinks (v2.2) to generated relative transcript levels in Fragments Per Kilobase of transcript per million mapped read (FPKM), and DESeq (v1.6.3) using a matched sample model was used to identify differentially expressed genes between the SCC and normal skin samples. Each gene of interest was selected by closest proximity to one of the eight novel risk variants; however, if a lead SNP was an eQTL in the skin tissue for a more distant gene, then this gene was chosen as well. Boxplot was used to visualize the expression of the SCC relative to normal skin of the genes sur-rounding the eight novel SNPs.
Fine mapping. We used GCTA-COJO to establish distinct association signals at the genome-wide significant loci with SCC susceptibility46. GCTA-COJO performs an approximate conditional analysis using association summary statistics from GWAS meta-analysis and the LD information estimated from a reference panel. For each locus, we defined a 2 Mb region encompassing 1 Mb from the lead SNP (using summary statistics) on both sides to ensure long-range genetic signals are not missed. Conditional independent variants that reach genome-wide significance level (the GCTA-COJO default level, 5 × 10−8) were considered as index SNPs for distinct association signals. We applied additionalfilters to association summary statistics and discarded variants with (i) MAF < 0.1%; (ii) ambiguous A/T and G/C alleles; and (iii) allele coding and frequency mismatches between genotypes in summary statistics and LD reference panel (implemented in GCTA-COJO). We defined the effective sample size for each cohort and used these estimates further in the analysis:
Neff¼ 4NcasesNcontrols= Ncasesð þ NcontrolsÞ: ð3Þ We used imputed genotypes in the Harvard cohort (the imputation quality R2>
0.3) as a reference panel for LD r measures (the Pearson correlation). We selected the Harvard cohort as a reference panel for LD r measures (the Pearson correlation), because it was the largest cohort of our meta-analysis, in which we have access to raw genotype data. We used imputed genotypes with the imputation quality R2> 0.347. The total number of individuals in the Harvard reference panel was 7403; the per-locus overlap between variants in summary statistics and reference panel was > 80% for variants with MAF > 0.01 and >50% for variants with MAF= 0.001–0.01. After applying the quality control, we had 19 of the 22 loci with lead SNPs passing the significance threshold (p < 5 × 10−8) and, thus, available for
the analysis. The two discarded loci had their lead SNPs with MAF < 1%, which werefiltered out likely due low coverage of the genotyping platforms or insufficient density of genotype imputation panels47. Another two loci in the MHC region (16p21.32) and MC1R (16q24.3) region were excluded to their complicated LD structures (Supplementary Table 6).
For each association signal from the conditional analyses by GCTA-COJO, we computed an approximate Bayes factor in favor of association on the basis of effect sizes and standard errors from the GWAS summary statistics within the 2 Mb region of the locus48. When loci showed a single-association signal, the summary statistics were taken from unconditional GWAS. When loci exhibited multiple association signals, the summary statistics were derived from the approximate conditional analysis adjusting for all other index variants in the region. The prior probabilities of the variant to be causal were assumed to be the same among all the variants and equal to 1/M, where M is the number of variants in the region.
For the ith variant the approximate Bayes factor is: BFi¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffi Vi Viþ ω s exp wβ 2 i 2ViðViþ wÞ ð4Þ
whereβiand Videnote the effect size and variance (the squared standard error) of the variant i from unconditional or approximate conditional association analysis. The parameterω denotes the prior variance in effects, which is set to 0.04 (Wakefield, 2007)48.
Then the posterior probability that the ith variant is a true association signal (PPA) is:
πi¼PMBFi
m¼1BFm ð5Þ
The 99% credible set is defined as the minimal number of variants with the cumulative PPA of 0.99. The procedure to compute the 99% credible set is accomplished in two steps: (i) order the variants in descending order of their PPA; (ii) include ordered variants until the cumulative PPA reaches 0.9949.
Stratified association analysis by photodistributed sites. According to the approach by Lin et al.50we estimated the heterogeneity of genetic effect size between high- and low-photoexposure site, considering overlapping controls used in high- and low-photodistributed site cohorts50.
Correlation of the genetic effects of per-SNP in high- and low-photodistributed site in each study is estimated by:
Corrð^β1; ^β2Þ ¼ n120 ffiffiffiffiffiffiffiffiffiffiffiffiffi n11n21 n10n20 r þ n121 ffiffiffiffiffiffiffiffiffiffiffiffiffin10n20 n11n21 r =pffiffiffiffiffiffiffiffiffin1n2¼ ffiffiffiffiffiffiffiffiffiffiffiffiffi n11n21 p ffiffiffiffiffiffiffiffiffi n1n2 p ð6Þ
Where ^β1is the estimate of log odds ratio of an individual SNP in high-photodistributed site in each study, ^β2is the estimate of log odds ratio of the SNP in low-photodistributed site in each study50.
n11, n10and n1are, respectively, the number of cases, the number of controls, and the total number of subjects in the high-photodistributed cohort and n21, n20 and n2are, respectively, the number of cases, the number of controls, and the total number of subjects in the low-photodistributed cohort.
Given that the controls in the high- and low-photodistributed site cohorts are totally overlapped, n120= n10= n20; whereas the cases are not shared: n121= 0.
The difference ^δ between the genetic effects of the SNP in high- and low-photodistributed site in each study is estimated by:
^δ ¼ b^β1 b^β2 ð7Þ The variance of ^δ in each study is estimated by:
Var ^ δ ¼ Var b^β1 þ Var ^β2 2Corr ^β1; ^β2 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Var b^β1 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Var b^β2 s v u u t ð8Þ Where Var b^β1
and Var ^ β2 are, respectively, the variances of ^β1and ^β2. The heterogeneity of genetic effect size between high- and low-photodistributed site for per-SNP in the overall six studies is tested byfixed effect meta-analysis of
^δi; Var b^δi
ð9Þ where i is each of the six studies.
Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Data availability
Data from 23andMe, Inc were made available under a data use agreement that protects participant privacy. Please contact dataset-request@23andme.com or visit
research.23andMe.com/collaborate for more information and to apply to access the data. Precomputed rankings and P-values for the top 10,000 SNPs included in the GWAS
meta-analysis are available in thefigshare repositoryhttps://doi.org/10.6084/m9.
figshare.1158832551. Any additional data (beyond those included in the main text and
Supplementary Information) that support thefindings of this study are available from the
corresponding author upon request.
Received: 26 May 2019; Accepted: 20 January 2020;
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Acknowledgements
We thank the research participants and employees of 23andMe for contributing to this work. In addition, we thank the participants and staff of the Nurses’ Health Study and the Health Professionals Follow-up Study, for their valuable contributions, as well as the following state cancer registries for their help: A.L., A.Z., A.R., C.A., C.O., C.T., D.E., F.L., G.A., I.D., I.L., I.N., I.A., K.Y., L.A., M.E., M.D., M.A., M.I., N.E., N.H., N.J., N.Y., N.C., N.D., O.H., O.K., O.R., P.A., R.I., S.C., T.N., T.X., V.A., W.A. and W.Y. We assume full responsibility for analyses and interpretation of these data. EQTL data described in this paper were obtained from the GTEx Portal on 06/01/2018. We thank L. Tryggvadottir and
G.H. Olafsdottir of the Icelandic Cancer Registry for assistance in the ascertainment of affected individuals. We thank D. Allain, V. Klee, and M. Bernhardt for ascertainment of cases and associated clinical data. The OSU Human Genetics Sample bank provided control samples. This work was supported in part by the National Human Genome Research Institute of the National Institutes of Health (grant number R44HG006981) and in part by NIH R01 CA49449, P01 CA87969, UM1 CA186107, UM1 CA167552, R03 CA219779, K23 CA211793 (K.Y.S.), and in part by Walther Cancer Foundation (J.H.). K.Y.S. is a Damon Runyon Clinical Investigator supported (in part) by the Damon Runyon Cancer Research Foundation. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University Rotterdam; Netherlands Organization for the Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly (RIDE); the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam.
Author contributions
K.Y.S. and J.H. designed the study. K.Y.S., Y.L., A.Z. G.T., R.D., A.R., L.P., S.N.S. and P.K. contributed data analyses. K.Y.S. and J.H. oversaw the study. K.Y.S., Y.L., R.D., A.Z. and S.N.S., contributed to the drafting of the paper. All authors, K.Y.S., Y.L., R.D., A.Z., G.T., A.R., L.M.P., W.W., P.A.K., A.U., T.N., A.E.T., J.H.O., B.S., K.T., E.J., A.S.W., P.K., S.N.S., K.S., M.M.A. and J.H critically reviewed the paper.
Competing interests
G.T., S.N.S. and K.S. are employees of deCODE Genetics.
Additional information
Supplementary informationis available for this paper at
https://doi.org/10.1038/s41467-020-14594-5.
Correspondenceand requests for materials should be addressed to K.Y.S. or J.H.
Peer review informationNature Communications thanks Wei Zheng and the other,
anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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